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aws_frauddetector_model_version

account_id

Type: STRING

arn

Type: STRING
Provider name: arn
Description: The model version ARN.

created_time

Type: STRING
Provider name: createdTime
Description: The timestamp when the model was created.

external_events_detail

Type: STRUCT
Provider name: externalEventsDetail
Description: The external events data details. This will be populated if the trainingDataSource for the model version is specified as EXTERNAL_EVENTS.

  • data_access_role_arn
    Type: STRING
    Provider name: dataAccessRoleArn
    Description: The ARN of the role that provides Amazon Fraud Detector access to the data location.
  • data_location
    Type: STRING
    Provider name: dataLocation
    Description: The Amazon S3 bucket location for the data.

ingested_events_detail

Type: STRUCT
Provider name: ingestedEventsDetail
Description: The ingested events data details. This will be populated if the trainingDataSource for the model version is specified as INGESTED_EVENTS.

  • ingested_events_time_window
    Type: STRUCT
    Provider name: ingestedEventsTimeWindow
    Description: The start and stop time of the ingested events.
    • end_time
      Type: STRING
      Provider name: endTime
      Description: Timestamp of the final ingested event.
    • start_time
      Type: STRING
      Provider name: startTime
      Description: Timestamp of the first ingensted event.

last_updated_time

Type: STRING
Provider name: lastUpdatedTime
Description: The timestamp when the model was last updated.

model_id

Type: STRING
Provider name: modelId
Description: The model ID.

model_type

Type: STRING
Provider name: modelType
Description: The model type.

model_version_number

Type: STRING
Provider name: modelVersionNumber
Description: The model version number.

status

Type: STRING
Provider name: status
Description: The status of the model version.

tags

Type: UNORDERED_LIST_STRING

training_data_schema

Type: STRUCT
Provider name: trainingDataSchema
Description: The training data schema.

  • label_schema
    Type: STRUCT
    Provider name: labelSchema
    • label_mapper
      Type: STRING
      Provider name: labelMapper
      Description: The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD, LEGIT) to the appropriate event type labels. For example, if “FRAUD” and “LEGIT” are Amazon Fraud Detector supported labels, this mapper could be: {“FRAUD” => [“0”], “LEGIT” => [“1”]} or {“FRAUD” => [“false”], “LEGIT” => [“true”]} or {“FRAUD” => [“fraud”, “abuse”], “LEGIT” => [“legit”, “safe”]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.
    • unlabeled_events_treatment
      Type: STRING
      Provider name: unlabeledEventsTreatment
      Description: The action to take for unlabeled events.
      • Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.
      • Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.
      • Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.
      • Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.
      By default, Amazon Fraud Detector ignores the unlabeled data.
  • model_variables
    Type: UNORDERED_LIST_STRING
    Provider name: modelVariables
    Description: The training data schema variables.

training_data_source

Type: STRING
Provider name: trainingDataSource
Description: The model version training data source.

training_result

Type: STRUCT
Provider name: trainingResult
Description: The training results.

  • data_validation_metrics
    Type: STRUCT
    Provider name: dataValidationMetrics
    Description: The validation metrics.
    • field_level_messages
      Type: UNORDERED_LIST_STRUCT
      Provider name: fieldLevelMessages
      Description: The field-specific model training validation messages.
      • content
        Type: STRING
        Provider name: content
        Description: The message content.
      • field_name
        Type: STRING
        Provider name: fieldName
        Description: The field name.
      • identifier
        Type: STRING
        Provider name: identifier
        Description: The message ID.
      • title
        Type: STRING
        Provider name: title
        Description: The message title.
      • type
        Type: STRING
        Provider name: type
        Description: The message type.
    • file_level_messages
      Type: UNORDERED_LIST_STRUCT
      Provider name: fileLevelMessages
      Description: The file-specific model training data validation messages.
      • content
        Type: STRING
        Provider name: content
        Description: The message content.
      • title
        Type: STRING
        Provider name: title
        Description: The message title.
      • type
        Type: STRING
        Provider name: type
        Description: The message type.
  • training_metrics
    Type: STRUCT
    Provider name: trainingMetrics
    Description: The training metric details.
    • auc
      Type: FLOAT
      Provider name: auc
      Description: The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
    • metric_data_points
      Type: UNORDERED_LIST_STRUCT
      Provider name: metricDataPoints
      Description: The data points details.
      • fpr
        Type: FLOAT
        Provider name: fpr
        Description: The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.
      • precision
        Type: FLOAT
        Provider name: precision
        Description: The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.
      • threshold
        Type: FLOAT
        Provider name: threshold
        Description: The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.
      • tpr
        Type: FLOAT
        Provider name: tpr
        Description: The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.
  • variable_importance_metrics
    Type: STRUCT
    Provider name: variableImportanceMetrics
    Description: The variable importance metrics.
    • log_odds_metrics
      Type: UNORDERED_LIST_STRUCT
      Provider name: logOddsMetrics
      Description: List of variable metrics.
      • variable_importance
        Type: FLOAT
        Provider name: variableImportance
        Description: The relative importance of the variable. For more information, see Model variable importance.
      • variable_name
        Type: STRING
        Provider name: variableName
        Description: The name of the variable.
      • variable_type
        Type: STRING
        Provider name: variableType
        Description: The type of variable.

training_result_v2

Type: STRUCT
Provider name: trainingResultV2
Description: The training result details. The details include the relative importance of the variables.

  • aggregated_variables_importance_metrics
    Type: STRUCT
    Provider name: aggregatedVariablesImportanceMetrics
    Description: The variable importance metrics of the aggregated variables. Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.
    • log_odds_metrics
      Type: UNORDERED_LIST_STRUCT
      Provider name: logOddsMetrics
      Description: List of variables’ metrics.
      • aggregated_variables_importance
        Type: FLOAT
        Provider name: aggregatedVariablesImportance
        Description: The relative importance of the variables in the list to the other event variable.
      • variable_names
        Type: UNORDERED_LIST_STRING
        Provider name: variableNames
        Description: The names of all the variables.
  • data_validation_metrics
    Type: STRUCT
    Provider name: dataValidationMetrics
    • field_level_messages
      Type: UNORDERED_LIST_STRUCT
      Provider name: fieldLevelMessages
      Description: The field-specific model training validation messages.
      • content
        Type: STRING
        Provider name: content
        Description: The message content.
      • field_name
        Type: STRING
        Provider name: fieldName
        Description: The field name.
      • identifier
        Type: STRING
        Provider name: identifier
        Description: The message ID.
      • title
        Type: STRING
        Provider name: title
        Description: The message title.
      • type
        Type: STRING
        Provider name: type
        Description: The message type.
    • file_level_messages
      Type: UNORDERED_LIST_STRUCT
      Provider name: fileLevelMessages
      Description: The file-specific model training data validation messages.
      • content
        Type: STRING
        Provider name: content
        Description: The message content.
      • title
        Type: STRING
        Provider name: title
        Description: The message title.
      • type
        Type: STRING
        Provider name: type
        Description: The message type.
  • training_metrics_v2
    Type: STRUCT
    Provider name: trainingMetricsV2
    Description: The training metric details.
    • ati
      Type: STRUCT
      Provider name: ati
      Description: The Account Takeover Insights (ATI) model training metric details.
      • metric_data_points
        Type: UNORDERED_LIST_STRUCT
        Provider name: metricDataPoints
        Description: The model’s performance metrics data points.
        • adr
          Type: FLOAT
          Provider name: adr
          Description: The anomaly discovery rate. This metric quantifies the percentage of anomalies that can be detected by the model at the selected score threshold. A lower score threshold increases the percentage of anomalies captured by the model, but would also require challenging a larger percentage of login events, leading to a higher customer friction.
        • atodr
          Type: FLOAT
          Provider name: atodr
          Description: The account takeover discovery rate. This metric quantifies the percentage of account compromise events that can be detected by the model at the selected score threshold. This metric is only available if 50 or more entities with at-least one labeled account takeover event is present in the ingested dataset.
        • cr
          Type: FLOAT
          Provider name: cr
          Description: The challenge rate. This indicates the percentage of login events that the model recommends to challenge such as one-time password, multi-factor authentication, and investigations.
        • threshold
          Type: FLOAT
          Provider name: threshold
          Description: The model’s threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.
      • model_performance
        Type: STRUCT
        Provider name: modelPerformance
        Description: The model’s overall performance scores.
        • asi
          Type: FLOAT
          Provider name: asi
          Description: The anomaly separation index (ASI) score. This metric summarizes the overall ability of the model to separate anomalous activities from the normal behavior. Depending on the business, a large fraction of these anomalous activities can be malicious and correspond to the account takeover attacks. A model with no separability power will have the lowest possible ASI score of 0.5, whereas the a model with a high separability power will have the highest possible ASI score of 1.0
    • ofi
      Type: STRUCT
      Provider name: ofi
      Description: The Online Fraud Insights (OFI) model training metric details.
      • metric_data_points
        Type: UNORDERED_LIST_STRUCT
        Provider name: metricDataPoints
        Description: The model’s performance metrics data points.
        • fpr
          Type: FLOAT
          Provider name: fpr
          Description: The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.
        • precision
          Type: FLOAT
          Provider name: precision
          Description: The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.
        • threshold
          Type: FLOAT
          Provider name: threshold
          Description: The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.
        • tpr
          Type: FLOAT
          Provider name: tpr
          Description: The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.
      • model_performance
        Type: STRUCT
        Provider name: modelPerformance
        Description: The model’s overall performance score.
        • auc
          Type: FLOAT
          Provider name: auc
          Description: The area under the curve (auc). This summarizes the total positive rate (tpr) and false positive rate (FPR) across all possible model score thresholds.
        • uncertainty_range
          Type: STRUCT
          Provider name: uncertaintyRange
          Description: Indicates the range of area under curve (auc) expected from the OFI model. A range greater than 0.1 indicates higher model uncertainity.
          • lower_bound_value
            Type: FLOAT
            Provider name: lowerBoundValue
            Description: The lower bound value of the area under curve (auc).
          • upper_bound_value
            Type: FLOAT
            Provider name: upperBoundValue
            Description: The upper bound value of the area under curve (auc).
    • tfi
      Type: STRUCT
      Provider name: tfi
      Description: The Transaction Fraud Insights (TFI) model training metric details.
      • metric_data_points
        Type: UNORDERED_LIST_STRUCT
        Provider name: metricDataPoints
        Description: The model’s performance metrics data points.
        • fpr
          Type: FLOAT
          Provider name: fpr
          Description: The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.
        • precision
          Type: FLOAT
          Provider name: precision
          Description: The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.
        • threshold
          Type: FLOAT
          Provider name: threshold
          Description: The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.
        • tpr
          Type: FLOAT
          Provider name: tpr
          Description: The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.
      • model_performance
        Type: STRUCT
        Provider name: modelPerformance
        Description: The model performance score.
        • auc
          Type: FLOAT
          Provider name: auc
          Description: The area under the curve (auc). This summarizes the total positive rate (tpr) and false positive rate (FPR) across all possible model score thresholds.
        • uncertainty_range
          Type: STRUCT
          Provider name: uncertaintyRange
          Description: Indicates the range of area under curve (auc) expected from the TFI model. A range greater than 0.1 indicates higher model uncertainity.
          • lower_bound_value
            Type: FLOAT
            Provider name: lowerBoundValue
            Description: The lower bound value of the area under curve (auc).
          • upper_bound_value
            Type: FLOAT
            Provider name: upperBoundValue
            Description: The upper bound value of the area under curve (auc).
  • variable_importance_metrics
    Type: STRUCT
    Provider name: variableImportanceMetrics
    • log_odds_metrics
      Type: UNORDERED_LIST_STRUCT
      Provider name: logOddsMetrics
      Description: List of variable metrics.
      • variable_importance
        Type: FLOAT
        Provider name: variableImportance
        Description: The relative importance of the variable. For more information, see Model variable importance.
      • variable_name
        Type: STRING
        Provider name: variableName
        Description: The name of the variable.
      • variable_type
        Type: STRING
        Provider name: variableType
        Description: The type of variable.